A method, system, electronic device, and storage medium for predicting the degree of tempering embrittlement.

Abstract

This invention discloses a method, system, electronic device, and storage medium for predicting the degree of temper embrittlement in hydrogenation reactors. The method involves acquiring temper embrittlement characterization indicators and influencing parameters of the hydrogenation reactor; performing dimensional analysis on the temper embrittlement characterization indicators and influencing parameters to obtain dimensionless expressions; performing nonlinear regression analysis based on the temper embrittlement characterization indicators, influencing parameters, and dimensionless expressions to determine prediction formulas for each temper embrittlement characterization indicator; and predicting the degree of temper embrittlement based on the prediction formulas for each temper embrittlement characterization indicator. This invention can improve the prediction accuracy of temper embrittlement in hydrogenation reactors.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of hydrogenation reactor temper embrittlement degree prediction, and in particular to a temper embrittlement degree prediction method and system, an electronic device and a storage medium. BACKGROUND

[0002] Hydrogenation reactors are core equipment in petroleum and coal chemical industry, and are widely used in hydrocracking, hydrofining, hydrodesulfurization and coal liquefaction processes. Hydrogenation reactors are operated in harsh conditions, such as high temperature, high pressure and hydrogen environment, and have material degradation problems such as temper embrittlement, hydrogen embrittlement and overlay layer peeling. Among them, temper embrittlement is the main damage mode of material degradation in the long-term service of hydrogenation reactors. Accurate evaluation of temper embrittlement is one of the important means to ensure the safe operation of hydrogenation reactors.

[0003] The degree of temper embrittlement is measured by the ductile-brittle transition temperature (DBTT), and there are two ways to determine the DBTT. One is to take the temperature corresponding to the 50% fiber rate of the impact test block fracture (FATT) as the DBTT, and the other is to take the temperature corresponding to the impact energy of 54J (Tr54) as the DBTT. At the same time, the difference between the FATT and Tr54 corresponding to the test evaluation time point and the factory out-of-factory time (△FATT and △Tr54) is also an index representing the degree of temper embrittlement DBTT. Tempering embrittlement is usually related to factors such as hydrogenation reactor material (brittleness sensitivity coefficient X / J factor), operating temperature, operating pressure, service time, etc. In addition, a large number of engineering experience and industry consensus show that the temper embrittlement degree of the base material and the weld of the hydrogenation reactor is different, and under the same service conditions, the temper embrittlement degree of the weld is significantly more serious than that of the base material. Therefore, in the determination of temper embrittlement, the temper embrittlement degree of the base material and the weld should be determined respectively, and the influence of factors such as material, operating parameters and service time should be considered.

[0004] The determination of the above FATT, Tr54, △FATT and △Tr54 can be achieved by experimental characterization and numerical prediction. Among them, the former is to place a hanging block in the reactor, and to take it out during regular inspection to determine the DBTT through tensile and impact tests. However, the regular inspection cycle is long (usually 3-4 years), and the hanging block is consumed in the test, so the number of hanging blocks is extremely limited in the later stage of the service of the hydrogenation reactor, and the safety problems caused by temper embrittlement in the later stage of the service of the hydrogenation reactor are more serious. Therefore, this method has the problems of long evaluation time interval and lack of evaluation hanging blocks in the later stage.

[0005] The current prediction of hydrogenation reactors mainly refers to the API 579-2016 standard published by the American Petroleum Institute, which uses 50%, 95% and 99% confidence limits to predict the DBTT embrittlement value FATT of the hydrogenation reactor:

[0006] FATT 99％ = -8.0043 x 10 -4 J 2 + 0.7267J - 15.416 (1)

[0007] FATT 95％ = -8.5424 x 10 -4 J 2 + 0.7746J - 48.782 (2)

[0008] FATT 50％ = -5.5147 x 10 -4 J 2 + 0.5757J - 77.321 (3)

[0009] Wherein J is a brittleness sensitivity coefficient calculated from material element content:

[0010] J = (w Si + w Mn ) x (w p + w Sn ) x 10 4 (4)

[0011] Wherein w Si is silicon content, w Mn is manganese content, w p is phosphorus content, and w Sn is tin content. The method has been widely used in engineering and plays an important role in evaluating temper embrittlement, but the method has the following problems: (1) the temper embrittlement of the base material and the weld is very different, and the same formula is used to predict the base material and the weld; (2) the prediction result of FATT is related to material factors (X / J coefficient), operation parameters (temperature and time), and service time factors, and the formula only considers the material factor J coefficient; (3) FATT, Tr54, △FATT and △Tr54 are all parameters representing DBTT, and only the prediction of FATT is currently available; (4) the dimension of FATT is temperature, and the J coefficient is a dimensionless quantity, the dimension of the formula is not unified, and the relationship between FATT and J coefficient is expressed in the form of a quadratic function, which does not conform to the actual situation. Therefore, there is a problem of inaccurate prediction of temper embrittlement of the hydrogenation reactor. SUMMARY

[0012] The purpose of the present application is to provide a temper embrittlement degree prediction method, system, electronic device and storage medium, so as to improve the prediction accuracy of the temper embrittlement of the hydrogenation reactor.

[0013] To achieve the above-mentioned purpose, the present application provides the following scheme:

[0014] A temper embrittlement degree prediction method comprises:

[0015] obtaining a temper embrittlement characterization index and an influence parameter of the hydrogenation reactor; the temper embrittlement characterization index includes a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block when manufactured, and a difference between a test evaluation time point and a temperature corresponding to an impact energy of 54J when manufactured; the influence parameter includes an operating temperature, an operating pressure, a service time, a wall thickness, a material density, and a brittleness sensitivity coefficient;

[0016] dimensional analysis is performed on the temper embrittlement characterization index and the influence parameter to obtain a dimensionless quantity expression;

[0017] nonlinear regression analysis is performed according to the temper embrittlement characterization index, the influence parameter, and the dimensionless quantity expression to determine a temper embrittlement characterization index prediction formula;

[0018] the temper embrittlement degree is predicted according to the temper embrittlement characterization index prediction formula.

[0019] Optionally, dimensional analysis is performed on the temper embrittlement characterization index and the influence parameter to obtain a dimensionless quantity expression, and the dimensional analysis specifically includes:

[0020] a data set of the temper embrittlement characterization index and the influence parameter is constructed;

[0021] dimensional analysis is performed on the data set to obtain a dimensionless quantity expression.

[0022] Optionally, nonlinear regression analysis is performed according to the temper embrittlement characterization index, the influence parameter, and the dimensionless quantity expression to determine a temper embrittlement characterization index prediction formula, and the nonlinear regression analysis specifically includes:

[0023] a function relationship between the temper embrittlement characterization index and the dimensionless quantity is constructed according to the temper embrittlement characterization index, the influence parameter, and the dimensionless quantity expression;

[0024] nonlinear regression is performed on the temper embrittlement characterization index according to the function relationship, the temper embrittlement characterization index, and the influence parameter to obtain a temper embrittlement characterization index prediction formula.

[0025] The present application also provides a temper embrittlement degree prediction system, which includes:

[0026] The acquisition module is used to acquire temper embrittlement characterization indexes and influence parameters of a hydrogenation reactor; the temper embrittlement characterization indexes include a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block when manufactured, and a difference between a test evaluation time point and a temperature corresponding to an impact energy of 54J when manufactured; and the influence parameters include an operating temperature, an operating pressure, a service time, a wall thickness, a material density and a brittleness sensitivity coefficient.

[0027] The dimension analysis module is used to perform dimension analysis on the temper embrittlement characterization indexes and the influence parameters to obtain a dimensionless quantity expression.

[0028] The nonlinear regression analysis module is used to perform nonlinear regression analysis according to the temper embrittlement characterization indexes, the influence parameters and the dimensionless quantity expression to determine a temper embrittlement characterization index prediction formula.

[0029] The prediction module is used to predict a temper embrittlement degree according to the temper embrittlement characterization index prediction formula.

[0030] Optionally, the dimension analysis module specifically includes:

[0031] The construction unit is used to construct a data set of the temper embrittlement characterization indexes and the influence parameters.

[0032] The dimension analysis unit is used to perform dimension analysis on the data set to obtain a dimensionless quantity expression.

[0033] Optionally, the nonlinear regression analysis module specifically includes:

[0034] The function relationship determination unit is used to construct a function relationship between the temper embrittlement characterization indexes and the dimensionless quantity according to the temper embrittlement characterization indexes, the influence parameters and the dimensionless quantity expression.

[0035] The nonlinear regression unit is used to perform nonlinear regression on the temper embrittlement characterization indexes according to the function relationship, the temper embrittlement characterization indexes and the influence parameters to obtain a temper embrittlement characterization index prediction formula.

[0036] The present application also provides an electronic device, comprising:

[0037] One or more processors;

[0038] A storage device having one or more programs stored thereon;

[0039] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0040] The application also provides a storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the method.

[0041] According to the specific embodiments of the application, the following technical effects are achieved.

[0042] The application obtains temper brittleness characterization indexes and influence parameters of a hydrogenation reactor; the temper brittleness characterization indexes include a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block when manufactured, and a difference between a test evaluation time point and a temperature corresponding to an impact energy of 54J when manufactured; the influence parameters include an operating temperature, an operating pressure, a service time, a wall thickness, a material density, and a brittleness sensitivity coefficient; dimensional analysis is performed on the temper brittleness characterization indexes and the influence parameters to obtain a dimensionless quantity expression; nonlinear regression analysis is performed according to the temper brittleness characterization indexes, the influence parameters, and the dimensionless quantity expression to determine a prediction formula of each temper brittleness characterization index. The temper brittleness degrees of the base material and the weld are respectively predicted by FATT, Tr54, AFATT, and ATr54, which is more in line with the actual situation that the temper brittleness of the base material and the weld is different and there are multiple characterization indexes of temper brittleness; relevant factors influencing the temper brittleness are comprehensively considered, and the prediction result is more accurate. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0044] Figure 1 The temper brittleness degree prediction method flowchart provided by the present application is shown in the figure.

[0045] Figure 2 The hydrogenation reactor chromium-molybdenum steel temper brittleness degree prediction method implementation flowchart is shown in the figure.

[0046] Figure 3 The base material temper brittleness degree FATT out-of-sample data verification chart is shown in the figure.

[0047] Figure 4 The weld temper brittleness degree FATT out-of-sample data verification chart is shown in the figure. DETAILED DESCRIPTION

[0048] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of the present application.

[0049] The present application aims to provide a temper embrittlement degree prediction method, system, electronic device and storage medium, so as to improve the prediction accuracy of temper embrittlement of a hydrogenation reactor.

[0050] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0051] As shown in Figure 1 The present application provides a temper embrittlement degree prediction method, comprising:

[0052] Step 101: obtaining temper embrittlement characterization indicators and influence parameters of a hydrogenation reactor; the temper embrittlement characterization indicators include a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block at the time of factory manufacturing, and a difference between a test evaluation time point and a temperature corresponding to an impact energy of 54J at the time of factory manufacturing; the influence parameters include operating temperature, operating pressure, service time, wall thickness, material density and brittleness sensitivity coefficient.

[0053] Step 102: performing dimension analysis on the temper embrittlement characterization indicators and the influence parameters to obtain a dimensionless quantity expression.

[0054] Step 102, specifically comprising: constructing a data set of the temper embrittlement characterization indicators and the influence parameters; performing dimension analysis on the data set to obtain a dimensionless quantity expression.

[0055] Step 103: performing nonlinear regression analysis according to the temper embrittlement characterization indicators, the influence parameters and the dimensionless quantity expression to determine a temper embrittlement characterization indicator prediction formula.

[0056] Step 103, specifically comprising:

[0057] constructing a functional relationship between the temper embrittlement characterization indicators and the dimensionless quantity according to the temper embrittlement characterization indicators, the influence parameters and the dimensionless quantity expression; performing nonlinear regression on the temper embrittlement characterization indicators according to the functional relationship, the temper embrittlement characterization indicators and the influence parameters to obtain a temper embrittlement characterization indicator prediction formula.

[0058] Step 104: Predict the degree of temper embrittlement according to the prediction formula of each of the temper embrittlement characterization indexes.

[0059] like Figure 2 As shown, this invention also provides a specific prediction process for the method of predicting the temper embrittlement degree of a hydrogenation reactor in practical applications. The technical solution disclosed in this invention provides a prediction of temper embrittlement in hydrogenation reactors that is closer to engineering practice, considers more comprehensive factors, and has higher prediction accuracy. It solves the following main problems existing in current temper embrittlement prediction: using the same formula for prediction of base material and weld, incomplete consideration of temper embrittlement factors, and inconsistent dimensions of prediction formulas. A method for predicting the temper embrittlement degree of chromium-molybdenum steel in a hydrogenation reactor based on dimensional analysis includes the following steps:

[0060] S1: Determine the characterization index and influencing parameters of tempering embrittlement and express the parameters in a dimensionless manner.

[0061] S2: Collect literature data to form a dataset of relevant parameters.

[0062] S3: Perform dimensional analysis to obtain the expression for the dimensionless quantity.

[0063] S4: Determine the specific functional form of the tempering embrittlement index and its dimensionless nature.

[0064] S5: Nonlinear regression analysis determines the specific prediction formulas for each tempering embrittlement index.

[0065] S6: Comparative verification analysis of the predicted results of various indicators of tempering embrittlement.

[0066] In step S1, based on the current understanding of temper embrittlement, the degree of temper embrittlement is evaluated by the ductile-brittle transition temperature (DBTT). Specific characterization indicators of DBTT include FATT, Tr54, ΔFATT, and ΔTr54. FATT is the temperature corresponding to a 50% fiber content in the fracture surface of the impact test specimen; Tr54 is the temperature corresponding to an impact energy of 54 J; and ΔFATT and ΔTr54 are the differences between FATT and Tr54 at the test evaluation time point and at the time of manufacturing. The parameters influencing the temper embrittlement index DBTT include operating temperature T0, operating pressure P0, and service time S. t The wall thickness t0, material density ρ, brittleness sensitivity coefficient J (expression as shown in equation (4)), and brittleness sensitivity coefficient X are expressed as follows:

[0067] X = (10w) p +5w Sb +4w Sn +w As ) / 100 (5)

[0068] Among them, w p For phosphorus content, w Sb For antimony content, wSn w is the tin content, w As w is the arsenic content.

[0069] In practical applications, the selected length L, mass M, time T and temperature K are basic dimensions, and the symbols, units and dimensional expressions of each influencing parameter and evaluation index are shown in Table 1:

[0070] Table 1 Characterization index of temper embrittlement, related parameters and their dimensional expressions

[0071]

[0072] In step S2, the DBTT and influencing factors related to temper embrittlement published globally are collected and formed into a data set. As much as possible, the literature published globally involving the parameters in step S1 is collected to form a data set, and the characteristics of the data set are shown in Table 2. For parameters not included in a certain literature (such as wall thickness), the average value of all literature data containing the parameter is taken as the parameter value.

[0073] Table 2 Data set of temper embrittlement characterization index and related parameters

[0074]

[0075] In practical applications, 24 global literatures are collected, and the literature names and sources are shown in Table 3. The data in the above literatures are arranged to form a data set, and the data set table header is shown in Table 2. The "main material" is the material type for manufacturing the hydrogen reactor, which usually includes 2.25Cr1Mo, 2.25Cr1Mo0.25V, 3Cr1Mo, 3Cr1Mo0.25V, etc. The "material type" includes base material and weld. In data processing, for parameters not included in a certain literature (such as wall thickness), the average value of all literature data containing the parameter is taken as the parameter value.

[0076] Table 3 Literature name and source of prediction data

[0077]

[0078]

[0079]

[0080] In step S3, according to the dimensional analysis π theorem, four dimensionless quantities Π1, Π2, Π3 and Π4 are obtained through dimensional analysis, and the expressions of Π1, Π2, Π3 and Π4 are as follows:

[0081] Π2=JΠ3=X

[0082] Specifically:

[0083] From Table 1, the expression of the dimensionless matrix A formed by the parameters affecting temper embrittlement and the temper embrittlement index DBTT is:

[0084]

[0085] The rank R(A) of this matrix is 4, and according to the dimensional analysis π theorem, four dimensionless quantities are formed. The solution obtained by solving the matrix equation Ay = 0 is:

[0086]

[0087] That is, the expression of the four dimensionless quantities formed is:

[0088] Π2 = JΠ3 = X

[0089] The relationship between the characterization index of the temper embrittlement index DBTT in step S4 and the above-mentioned parameters is expressed in the form of a power function product, where a, b, c, and d are undetermined coefficients.

[0090]

[0091] Specifically:

[0092] There is a function association between DBTT and the above-mentioned four dimensionless quantities, where f is the function mapping relationship:

[0093] Π1 = f(Π2, Π3, Π4)

[0094] That is:

[0095]

[0096] For the prediction of temper embrittlement, the specific function form adopts the form of a power function product, where a, b, c, and d are undetermined coefficients.

[0097]

[0098] In step S5, in the prediction formula obtained by nonlinear regression of FATT, Tr54, ΔFATT, and ΔTr54, the above-mentioned four indexes and the X coefficient, the J coefficient, and the service time S t have the characteristics of increasing functions. According to the collected global literature temper embrittlement data set, the undetermined coefficients in the expressions of FATT, Tr54, ΔFATT, and ΔTr54 are determined by nonlinear regression analysis.

[0099] In practical applications, the data set in step S2 is taken as input data to perform nonlinear regression on the DBTT indexes of temper embrittlement (FATT, Tr54, AFATT and ATr54) to obtain specific expressions of the DBTT indexes and the dimensionless quantity, as shown in Table 4. Wherein R 2 is the goodness of fit, and the variable range is the parameter value range involved in the corresponding literature.

[0100] Table 4 Dimensionless prediction formula of each index of DBTT of temper embrittlement

[0101]

[0102]

[0103]

[0104] According to the current understanding of temper embrittlement, the increasing and decreasing relationships of the DBTT indexes with temperature and wall thickness are uncertain, but it has been agreed that the DBTT increases with the brittleness sensitivity coefficients (X, J) and service time. The relationships of the above-mentioned DBTT indexes with X, J and S t well reflect the characteristics, indicating the rationality of the physical meaning of the obtained prediction formula.

[0105] In step S6, the prediction results are verified, which is divided into in-sample data verification and out-of-sample data verification, wherein the in-sample data verification takes the average error as an index for comparison, and the average error Err is expressed as follows:

[0106]

[0107] Wherein n is the number of data sets, i represents the data in the ith group (1≤i≤n), x i,cal. is the predicted value of the ith DBTT, and x i,exp. is the experimental value of the ith DBTT.

[0108] In practical applications, the in-sample data is used to predict FATT according to the prediction formula with confidence limits of 99%, 95% and 50% in API579 and the formula in the above-mentioned embodiments of the present application (only the prediction of FATT in API579, and the predictions of other indexes AFATT, Tr54 and ATr54 are not included, so it is not listed), and the obtained average error results are shown in Table 5, and the results show that the prediction method proposed in the present application significantly reduces the prediction error of FATT.

[0109] Table 5 Comparison of temper embrittlement prediction results of in-sample data

[0110]

[0111]

[0112] At the same time, the prediction formula is verified by out-of-sample data, and the out-of-sample data is from American Petroleum Institute (API), Japan Iron and Steel Works (JSW) and South Korea Chevron Oil Company (data source: Effect of temper and hydrogen embrittlement on mechanical properties of 2,25Cr el Mo steel grades-Application to Minimum Pressurizing Temperature (MPT) issues. Part I: General considerations & materials' properties [J]. International Journal of Pressure Vessels and Piping, 2013, 110: 17-23). Figure 3 and Figure 4 The comparison result of the out-of-sample experimental data of the base material and the weld is that when the J coefficient is less than 200 (on the one hand, the prediction data of the present application are all less than 200, and on the other hand, the J coefficient of the manufactured hydrogenation reactor is usually less than 200), the prediction formula proposed by the present application is more consistent with the experimental data.

[0113] In summary, the present application provides a temper embrittlement degree prediction method based on dimensional analysis, which respectively predicts the DBTT indicators of the base material and the weld temper embrittlement, comprehensively considers the influencing factors of temper embrittlement, obtains the prediction formula with unified dimensions, and can more accurately evaluate the degree of temper embrittlement of the hydrogenation reactor during long-term service. By systematically collecting the data related to temper embrittlement in the published literature worldwide, comprehensively considering the influencing factors of the temper embrittlement of the hydrogenation reactor, and based on dimensional analysis, a prediction method for the temper embrittlement degree of the chromium-molybdenum steel of the hydrogenation reactor is proposed to significantly improve the prediction accuracy of the temper embrittlement of the hydrogenation reactor.

[0114] The working principle and beneficial effects of the present application are:

[0115] (1) The temper embrittlement degree of the base material and the weld is respectively predicted by FATT, Tr54, △FATT and △Tr54, which is more consistent with the actual situation that the temper embrittlement of the base material and the weld is different and there are multiple characterization indicators of temper embrittlement.

[0116] (2) The related factors affecting temper embrittlement are comprehensively considered, and the prediction result is more accurate.

[0117] (3) The obtained prediction formula has unified dimensions, correct physical meaning and is consistent with the current understanding of temper embrittlement.

[0118] The application further provides a temper embrittlement degree prediction system, comprising:

[0119] An acquisition module is configured to acquire temper embrittlement characterization indexes and influence parameters of a hydrogenation reactor; the temper embrittlement characterization indexes comprise a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54 J, a difference between a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block when the hydrogenation reactor is manufactured, and a difference between the test evaluation time point and a temperature corresponding to an impact energy of 54 J; and the influence parameters comprise an operating temperature, an operating pressure, a service time, a wall thickness, a material density, and a brittleness sensitivity coefficient.

[0120] A dimension analysis module is configured to perform dimension analysis on the temper embrittlement characterization indexes and the influence parameters to obtain a dimensionless quantity expression.

[0121] A nonlinear regression analysis module is configured to perform nonlinear regression analysis on the temper embrittlement characterization indexes, the influence parameters, and the dimensionless quantity expression to determine a temper embrittlement characterization index prediction formula.

[0122] A prediction module is configured to predict a temper embrittlement degree according to the temper embrittlement characterization index prediction formula.

[0123] As an optional implementation, the dimension analysis module specifically comprises:

[0124] A construction unit is configured to construct a data set of the temper embrittlement characterization indexes and the influence parameters.

[0125] A dimension analysis unit is configured to perform dimension analysis on the data set to obtain a dimensionless quantity expression.

[0126] As an optional implementation, the nonlinear regression analysis module specifically comprises:

[0127] A function relationship determination unit is configured to construct a function relationship between a temper embrittlement characterization index and a dimensionless quantity according to the temper embrittlement characterization indexes, the influence parameters, and the dimensionless quantity expression.

[0128] A nonlinear regression unit is configured to perform nonlinear regression on the temper embrittlement characterization index according to the function relationship, the temper embrittlement characterization index, and the influence parameters to obtain a temper embrittlement characterization index prediction formula.

[0129] The application further provides an electronic device, comprising:

[0130] One or more processors.

[0131] A storage device having one or more programs stored thereon.

[0132] The one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described.

[0133] The application further provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method as described.

[0134] The various embodiments are described in a progressive manner in the specification, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between various embodiments can be mutually referred to. For the system disclosed by the embodiments, since it corresponds to the method disclosed by the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0135] The principles and implementation manners of the application are described by using specific examples in the specification, and the above embodiment description is only used to help understand the method of the application and its core idea; meanwhile, for the general technical personnel in the field, the specific implementation manners and application ranges can be changed according to the idea of the application. In conclusion, the content of the specification should not be understood as the limitation of the application.

Claims

1. A method of predicting the degree of temper embrittlement, characterized by, The method comprises the following steps: obtaining temper brittleness characterization indexes and influence parameters of the hydrogenation reactor; the temper brittleness characterization indexes comprise a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a temperature corresponding to a 50% fiber rate of a fracture of an impact test block at a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block at a manufacturing factory time, and a difference between a temperature corresponding to an impact energy of 54J at a test evaluation time point and a temperature corresponding to an impact energy of 54J at a manufacturing factory time; the influence parameters comprise an operating temperature, an operating pressure, a service time, a wall thickness, a material density, and a brittleness sensitivity coefficient; dimensional analysis is performed on the temper brittleness characterization indexes and the influence parameters to obtain a dimensionless quantity expression; According to the temper embrittlement characterization index, the influence parameter and the dimensionless quantity expression, a function relationship between the temper embrittlement characterization index and the dimensionless quantity is constructed; and a specific relationship is Wherein a, b, c and d are undetermined coefficients, DBTT is the temper embrittlement index, t0 is the wall thickness, T0 is the operating temperature, J is the brittleness sensitivity coefficient, X is the brittleness sensitivity coefficient, P0 is the operating pressure, S t is the service time, and p is the material density. nonlinear regression is performed on the temper brittleness characterization indexes according to the function relationship, the temper brittleness characterization indexes, and the influence parameters to determine undetermined coefficients in the relationship formula, and temper brittleness characterization index prediction formulas are obtained; the temper brittleness degree is predicted according to the temper brittleness characterization index prediction formulas.

2. The method of predicting the degree of temper embrittlement according to claim 1, characterized by, The dimensional analysis of the temper brittleness characterization indexes and the influence parameters comprises the following steps: a data set of the temper brittleness characterization indexes and the influence parameters is constructed; dimensional analysis is performed on the data set to obtain a dimensionless quantity expression.

3. A temper embrittlement degree prediction system characterized by, The temper brittleness degree prediction system applies the temper brittleness degree prediction method in any one of claims 1-2, and the temper brittleness degree prediction system comprises: an obtaining module configured to obtain temper brittleness characterization indexes and influence parameters of the hydrogenation reactor; the temper brittleness characterization indexes comprise a temperature corresponding to a 50% fiber rate of a fracture of an impact test block, a temperature corresponding to an impact energy of 54J, a difference between a temperature corresponding to a 50% fiber rate of a fracture of an impact test block at a test evaluation time point and a temperature corresponding to a 50% fiber rate of a fracture of an impact test block at a manufacturing factory time, and a difference between a temperature corresponding to an impact energy of 54J at a test evaluation time point and a temperature corresponding to an impact energy of 54J at a manufacturing factory time; the influence parameters comprise an operating temperature, an operating pressure, a service time, a wall thickness, a material density, and a brittleness sensitivity coefficient; a dimensional analysis module configured to perform dimensional analysis on the temper brittleness characterization indexes and the influence parameters to obtain a dimensionless quantity expression; a nonlinear regression analysis module configured to perform nonlinear regression analysis according to the temper brittleness characterization indexes, the influence parameters, and the dimensionless quantity expression to determine temper brittleness characterization index prediction formulas; a prediction module configured to predict a temper brittleness degree according to the temper brittleness characterization index prediction formulas.

4. The temper brittleness degree prediction system according to claim 3, characterized by, The dimensional analysis module comprises the following steps: a construction unit configured to construct a data set of the temper brittleness characterization indexes and the influence parameters; a dimensional analysis unit configured to perform dimensional analysis on the data set to obtain a dimensionless quantity expression.

5. The temper brittleness degree prediction system according to claim 3, characterized by, The nonlinear regression analysis module comprises the following steps: a function relationship determination unit configured to construct a function relationship between temper brittleness characterization indexes and dimensionless quantities according to the temper brittleness characterization indexes, the influence parameters, and the dimensionless quantity expression. A nonlinear regression unit is configured to perform nonlinear regression on the tempering brittleness characterization index according to the function relationship, the tempering brittleness characterization index and the influence parameter, to obtain a tempering brittleness characterization index prediction formula.

6. An electronic device, comprising: comprising: one or more processors; a memory device having stored thereon one or more programs; when the one or more programs are executed by the one or more processors, the one or more programs cause the one or more processors to implement the method according to any one of claims 1 to 2.

7. A storage medium, characterized by a computer program is stored thereon, wherein the computer program is executed by a processor to implement the method according to any one of claims 1 to 2.

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